Efficient nonlinear programming algorithms for chemical process control and operations

Nonlinear programming (NLP) has been a key enabling tool for model-based decision-making in the chemical industry for over 50 years. Opti-mization is frequently applied in numerous ar-eas of chemical engineering including the de-velopment of process models from experimen-tal data, design of process flowsheets and equip-ment, planning and scheduling of chemical pro-cess operations, and the analysis of chemical pro-cesses under uncertainty and adverse conditions. These off-line tasks frequently require the solu-tion of NLPs formulated with detailed, lareg-scale process models. More recently, these tasks are complemented by time-critical, on-line optimization problem